The selection of agency as a rhetorical device: Opening up the scene of dialogue through ventriloquism
Bibliographic record
Abstract
I propose to open up the dialogic scene by showing that a dialogue is never just about discourse and language. It is also about facts, principles, passions, values, ideologies, collectives, worldviews, etc. that can (or cannot) make a difference, i.e., do something, in a given interaction. According to this approach, dialogue is one of the most important phonation devices through which a plethora of ‘things’ – which I call actants – can come to act from a distance. Showing that these actants can be rhetorically mobilized in a given interaction allows me to account for phenomena of ‘ventriloquism,’ that is, the various ways by which human interactants make certain entities (collectives, procedures, policies, ideologies, etc.) speak in their name and vice versa. We will see that this way of dislocating the dialogic scene allows us to address thoroughly the question of power and authority, a question that tends to be relatively downplayed by dialogue analysts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".